code: def test_arima_setup(params, X):
      """Checks if parameters are passed to Auto-Arima correctly"""
      coverage = 0.99
      model = AutoArimaEstimator(
          score_func=mean_squared_error,
          coverage=coverage,
          null_model_params=None,
          **params,
      )
  
      # set_params must be able to replicate the init
      model2 = AutoArimaEstimator()
      model2.set_params(
          **dict(
              score_func=mean_squared_error,
              coverage=coverage,
              null_model_params=None,
              **params,
          )
      )
      assert model2.__dict__ == model.__dict__
  
      model.fit(X)
      direct_model = AutoARIMA(**params)
  
      model_params = model.model.__dict__
      direct_model_params = direct_model.__dict__
  
      assert model_params["start_p"] == direct_model_params["start_p"]
      assert model_params["d"] == direct_model_params["d"]
      assert model_params["start_q"] == direct_model_params["start_q"]
      assert model_params["max_p"] == direct_model_params["max_p"]
      assert model_params["max_d"] == direct_model_params["max_d"]
      assert model_params["max_q"] == direct_model_params["max_q"]
      assert model_params["start_P"] == direct_model_params["start_P"]
      assert model_params["D"] == direct_model_params["D"]
      assert model_params["start_Q"] == direct_model_params["start_Q"]
      assert model_params["max_P"] == direct_model_params["max_P"]
      assert model_params["max_D"] == direct_model_params["max_D"]
      assert model_params["max_Q"] == direct_model_params["max_Q"]
      assert (
          model_params["max_order"] == direct_model_params["max_order"]
      )
      assert model_params["m"] == direct_model_params["m"]
      assert model_params["seasonal"] == direct_model_params["seasonal"]
      assert (
          model_params["stationary"]
          == direct_model_params["stationary"]
      )
      assert (
          model_params["information_criterion"]
          == direct_model_params["information_criterion"]
      )
      assert model_params["alpha"] == direct_model_params["alpha"]
      assert model_params["test"] == direct_model_params["test"]
      assert (
          model_params["seasonal_test"]
          == direct_model_params["seasonal_test"]
      )
      assert model_params["stepwise"] == direct_model_params["stepwise"]
      assert model_params["n_jobs"] == direct_model_params["n_jobs"]
      assert (
          model_params["start_params"]
          == direct_model_params["start_params"]
      )
      assert model_params["trend"] == direct_model_params["trend"]
      assert model_params["method"] == direct_model_params["method"]
      assert model_params["maxiter"] == direct_model_params["maxiter"]
      assert (
          model_params["offset_test_args"]
          == direct_model_params["offset_test_args"]
      )
      assert (
          model_params["seasonal_test_args"]
          == direct_model_params["seasonal_test_args"]
      )
      assert (
          model_params["suppress_warnings"]
          == direct_model_params["suppress_warnings"]
      )
      assert (
          model_params["error_action"]
          == direct_model_params["error_action"]
      )
      assert model_params["trace"] == direct_model_params["trace"]
      assert model_params["random"] == direct_model_params["random"]
      assert (
          model_params["random_state"]
          == direct_model_params["random_state"]
      )
      assert model_params["n_fits"] == direct_model_params["n_fits"]
      assert (
          model_params["out_of_sample_size"]
          == direct_model_params["out_of_sample_size"]
      )
      assert model_params["scoring"] == direct_model_params["scoring"]
      assert (
          model_params["scoring_args"]
          == direct_model_params["scoring_args"]
      )
      assert (
          model_params["with_intercept"]
          == direct_model_params["with_intercept"]
      )
      assert model_params["kwargs"] == direct_model_params["kwargs"]
  
imports: import numpy
  import pandas
  import pytest
  from pmdarima.arima import AutoARIMA
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from testfixtures import LogCapture
  from greykite.common.constants import LOGGER_NAME
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.evaluation import calc_pred_err
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.sklearn.estimator.auto_arima_estimator import AutoArimaEstimator
label_desc: Use image node_id 5 for calling a global function with example usage: test_arima_setup(params, X) without return types